code: def conv_act_layer_dw(
      from_layer,
      name,
      num_filter,
      kernel=(1, 1),
      pad=(0, 0),
      stride=(1, 1),
      act_type="relu",
      bias_wd_mult=0.0,
  ):
      assert kernel[0] == 3
      weight = mx.symbol.Variable(
          name="{}_weight".format(name),
          init=mx.init.Normal(0.01),
          attr={"__lr_mult__": "1.0"},
      )
      bias = mx.symbol.Variable(
          name="{}_bias".format(name),
          init=mx.init.Constant(0.0),
          attr={"__lr_mult__": "2.0", "__wd_mult__": str(bias_wd_mult)},
      )
      conv = mx.symbol.Convolution(
          data=from_layer,
          kernel=kernel,
          pad=pad,
          stride=stride,
          num_filter=num_filter,
          num_group=num_filter,
          name="{}".format(name),
          weight=weight,
          bias=bias,
      )
      conv = mx.sym.BatchNorm(
          data=conv,
          fix_gamma=False,
          eps=2e-5,
          momentum=0.9,
          name=name + "_bn",
      )
      if len(act_type) > 0:
          relu = mx.symbol.Activation(
              data=conv,
              act_type=act_type,
              name="{}_{}".format(name, act_type),
          )
      else:
          relu = conv
      return relu
  
file_path: insightface/detection/retinaface/rcnn/symbol/symbol_mnet.py
imports: import mxnet
  import mxnet.ndarray
  import mxnet.gluon
  import mxnet.gluon.nn
  import mxnet.autograd
  import numpy
  from rcnn.config import config
  from rcnn.PY_OP import rpn_fpn_ohem3
  from rcnn.symbol.symbol_common import get_sym_train
label_desc: Use image node_id 2 for calling a global function with example usage: conv_act_layer_dw(from_layer, name, num_filter, kernel, pad, stride, act_type, bias_wd_mult) and returns: relu